The Monitoring and Control of Government Towards Contracted-out Public Service Providers: The Case Study of Saudi Arabia
Bibliographic record
Abstract
The process of controlling contracted-out public services has attracted academic studies, however, very few studies have covered this topic in relation to developing countries, such as Saudi Arabia. Saudi Arabia started Vision 2030 aiming to improve the performance of the public sector. This vision relies in large part on the private sector, especially privatization and contracting-out of public services. As the government retains accountability for the quality of the services, they must monitor public-service contracts in order to achieve their objectives. Therefore, drawing on Structuration Theory (ST), which is novel for this context, this study aims to explore how the relationship between the government and public service providers is associated with controls and monitoring systems put in place. Few studies have examined these areas in relation to the public sector in detail. To bridge these gaps in previous accounting studies, 33 interviews were conducted, and relevant documents were collected. Two public services (public transportation and elderly care) were examined in two Saudi Arabian cities, Riyadh and Dammam, meaning that a total of four comparative case studies were explored. This is another novelty as this study attempts to broaden the understanding of controls and relations by investigating multiple studies. The results showed that control mechanisms were utilized differently by government actors at various levels and connected with the relationships between government and providers. Processual/relational variables played key roles, and they were strictly linked with the control systems. Different perspectives with regard to the enabling and constraining role of controls were revealed. Some patterns between controls and ST variables were discovered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".